Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) with external knowledge retrieval, improving factual accuracy and knowledge coverage. However, existing RAG approaches face a fundamental trade-off when handling complex reasoning: while traditional iterative retrieval methods offer flexibility, their local perspective limits their ability to establish global knowledge connections. In contrast, structure-augmented RAG methods capture global relationships but incur significant construction costs. To fill in this gap, we propose MGranRAG, an innovative framework designed to integrate precise local retrieval with structured global reasoning. Our approach circumvents expensive semantic extraction by employing a lightweight contextual hierarchical graph, effectively combining the local adaptability of iterative retrieval with the global consistency of structured knowledge. The framework adopts a novel iterative optimization scheme: at the local level, the LLM identifies multi-granular contextual evidence, such as key sentences and phrases, within retrieved passages to refine retrieval. At the global level, these multi-granularity evidence nodes are then mapped and propagated within the structured hierarchical graph, enabling the diffusion of rich contextual information at different levels to introduce global semantic constraints and reorder retrieval results. This coordination between local and global iterative processes dynamically balances retrieval accuracy and contextual coherence. Experimental results on challenging multi-hop and open-domain question answering datasets show that our proposal achieves new state-of-the-art performance in both retrieval and answer accuracy.

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, temporal reasoning, particularly under complex temporal constraints, remains a major challenge. To this end, existing approaches have explored symbolic methods, which encode temporal structure explicitly, and reflective mechanisms, which revise reasoning errors through multi-step inference. Nonetheless, symbolic approaches often underutilize the reasoning capabilities of LLMs, while reflective methods typically lack structured temporal representations, which can result in inconsistent or hallucinated reasoning. As a result, even when the correct temporal context is available, LLMs may still misinterpret or misapply time-related information, leading to incomplete or inaccurate answers. To address these limitations, in this work, we propose Neuro-Symbolic Temporal Reasoning (NeSTR), a novel framework that integrates structured symbolic representations with hybrid reflective reasoning to enhance the temporal sensitivity of LLM inference. NeSTR preserves explicit temporal relations through symbolic encoding, enforces logical consistency via verification, and corrects flawed inferences using abductive reflection. Extensive experiments on diverse temporal question answering benchmarks demonstrate that NeSTR achieves superior zero-shot performance and consistently improves temporal reasoning without any fine-tuning, showcasing the advantage of neuro-symbolic integration in enhancing temporal understanding in large language models.

Entity matching is a fundamental task in a wide range of retrieval and knowledge applications, aiming to identify whether two objects correspond to the same real-world entity across heterogeneous sources. Typical variants include entity resolution (ER), entity linking (EL), and entity alignment (EA). While recent unified matchers have made progress through multi-task training with comprehensive annotations, real-world pipelines often operate under scarce supervision, where labeled data is incomplete and fails to cover the full spectrum of matching scenarios. In this regime, supervised unified models degrade substantially, and deployable compact LLMs remain unreliable: lightweight fine-tuning and in-context learning yield inconsistent behavior and can even exhibit negative effects under scenario shifts. To fill in this gap, we propose øurs, a meta-rule induction and retrieval framework for unified entity matching under scarce supervision. Instead of relying on parametric adaptation, øurs converts limited supervision into explicit natural-language rules, abstracts them into reusable meta-rules via hierarchical clustering, and retrieves the most relevant meta-rules to guide the LLM's inference for each input instance. This design improves robustness by grounding decisions on explicit and reusable evidence, instead of relying solely on implicit adaptation or prompt demonstrations. Extensive experiments show that øurs achieves state-of-the-art performance on unified entity matching under scarce supervision.

Entity Matching (EM) is a fundamental task in data integration, traditionally studied over structured data such as tables and knowledge graphs. In modern repositories, real-world objects are often represented across multiple modalities, including structured entities with symbolic attributes and visual entities in image-centric collections. This motivates cross-modal entity matching, which aims to identify visual-structured entity pairs that refer to the same object. Existing methods typically rely on pretrained vision-language models to compute entity pair similarity and derive correspondence via local ranking, which, however, can be unreliable given noisy and ambiguous cross-modal data and may produce globally inconsistent correspondences across related entities. Multimodal large language models (MLLMs) offer richer cross-modal cues for matching, but exhaustive MLLM reasoning over large candidate spaces is prohibitively expensive. To address these limitations, in this work, we propose øurq, a blackboard-based multi-agent framework for zero-shot cross-modal entity matching that performs iterative, self-correcting refinement by fusing multiple matching signals, explicitly regulating global consistency, and selectively invoking an MLLM only for hard cases. We further construct two new benchmarks from real-world visual and structured data. Extensive experiments show that øurq consistently outperforms competitive baselines, providing an effective solution for zero-shot cross-modal entity matching. We release our data and code at https://github.com/Q-17/CoMCo.

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research.

Framing effect is a well-known cognitive bias in which individuals' responses to the same underlying question vary depending on how the question is phrased. Recent studies suggest that large language models (LLMs) also exhibit framing effects, but existing work has primarily replicated psychological experiments using hand-crafted prompts, leaving their impact on practical downstream tasks underexplored. To fill in the gap, in this paper, we conduct a systematic empirical investigation into framing effects in LLMs across multiple real-world downstream tasks. We construct semantically equivalent prompts with positive and negative framings and evaluate a wide range of LLMs under these conditions. We uncover several behavioral regularities of framing effects in LLMs, among which the most notable one is a consistent response asymmetry: LLMs find answering ''yes'' harder than ''no''. That is, LLMs tend to issue affirmative responses (i.e., ''yes'') only when they are highly confident, while they incline to answer negatively (i.e., ''no'') under uncertainty. We interpret this asymmetry through the lens of Error Management Theory (EMT), which posits that rational agents adopt risk-averse strategies to minimize the more costly error. We empirically show that this behavior is partially attributable to a statistical imbalance in the frequency of positive versus negative framing cues in pretraining corpora. Furthermore, we demonstrate that the framing-induced bias in LLMs can inform prompt engineering and active in-context learning, i.e., using framing-sensitive samples as demonstrations can improve model performance. Finally, we offer a preliminary strategy to mitigate the framing effect, i.e., injecting debiasing instructions, which shows promise. In all, our work uncovers a fundamental behavioral bias in LLMs and offers practical guidance for their reliable deployment across downstream tasks.

Multi-modal entity alignment (MMEA) is a long-standing task that aims to discover identical entities between different multi-modal knowledge graphs (MMKGs). However, most of the existing MMEA datasets consider the multi-modal data as the attributes of textual entities, while neglecting the correlations among the multi-modal data and do not fit in the real-world scenarios well. In response, in this work, we establish a novel yet practical MMEA dataset, i.e. NMMEA, which models multi-modal data (e.g., images) equally as textual entities in the MMKG. Due to the introduction of multi-modal data, NMMEA poses new challenges to existing MMEA solutions, i.e., heterogeneous structural representation learning and cross-modal alignment inference. Hence, we put forward a simple yet effective solution, CrossEA, which can effectively learn the structural information of entities by considering both intra-modal and cross-modal relations, and further infer the similarity of different types of entity pairs. Extensive experiments validate the significance of NMMEA, where CrossEA can achieve superior performance in contrast to competitive methods on the proposed dataset.

Knowledge Graph Alignment (KGA) identifies elements in different knowledge graphs (KGs) that refer to the same real-world object. It is a key step towards improving the coverage of KGs, which in turn can better facilitate downstream tasks. However, there is currently no interactive tool to support KGA research, particularly for visualizing alignment results, hence limiting the understanding of the procedure and also the development of more advanced solutions. To fill in this gap, in this paper, we introduce IKGA, an interactive visualization tool for KGA, which visualizes the alignment process by integrating various algorithms of representation learning and alignment inference-two key steps in KGA. The system allows users to observe KGA results, compare existing algorithms, and apply to domain-specific KGs at hand.

Although various graph contrastive learning (GCL) techniques have been employed to generate augmented views and maximize their mutual information, current solutions only consider the pairwise relationships based on edges, neglecting the high-order information that can help generate more informative augmented views and make better contrast. To fill in this gap, we propose to leverage hyperedge to facilitate GCL, as it connects two or more nodes and can model high-order relationships among multiple nodes. More specifically, hyperedges are constructed based on the original graph. Then, we conduct node-level Page Rank based on hyperedges and hyperedge-level PageRank based on nodes to generate augmented views. As to the contrasting stage, different from existing GCL methods that simply treat the corresponding nodes of the anchor in different views as positives and overlook certain nodes strongly associated with the anchor, we build the positives and negatives based on hyperedges, where whether a node is a positive is determined by the number of hyperedges it coexists with the anchor. We compare our hyperedge GCL with state-of-the-art methods on downstream tasks, and the empirical results validate the superiority of our proposal. Further experiments on graph augmentation and graph contrastive loss also demonstrate the effectiveness of the proposed modules.

Highly Heterogeneous Entity Alignment (HHEA) represents a more realistic application scenario of Entity Alignment (EA). This challenging task aims to align equivalent entities between highly heterogeneous knowledge graphs (HHKGs) with significant differences in structure, scale, and overlap. In practice, obtaining labeled data for HHEA is often difficult, necessitating research into unsupervised HHEA. This involves addressing several challenges, including the difficulty in capturing structural and semantic associations between HHKGs, the absence of explicit HHEA paradigms, and the high time and computational costs. Unfortunately, there is no solution for unsupervised HHEA. To bridge this gap, this paper formally investigates the unsupervised HHEA problem and proposes an effective unsupervised HHEA solution, AdaCoAgentEA, which addresses the challenges of unsupervised HHEA from the perspective of multi-agent collaboration. Specifically, we design an adaptive collaboration framework with three functional areas powered by multi-agent LLMs and small models, effectively eliminating dependence on labeled data while capturing structural and semantic correlations between HHKGs. Furthermore, we design a suite of optimization tools for AdaCoAgentEA, including meta-alignment mechanisms and communication protocols, which facilitate effective associations between HHKGs and provide explicit HHEA paradigms while reducing time and computational costs. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance in both unsupervised HHEA and classic EA tasks across five datasets, rivaling fully supervised models while maintaining high efficiency and scalability.

Abstract Knowledge tracing (KT) refers to the problem of predicting a learner’s future performance based on their past performance in education. Recently, attention-based sequence modeling methods achieve impressive predictive performance. However, existing solutions merely consider one single sequence modeling method, which might fail to capture the comprehensive state of knowledge across long sequences. In this paper, we propose D ual S equence M odeling for K nowledge T racing (DSMKT). DSMKT aims to enhance the modeling of a learner’s long-term profile by collaborating two sequence modeling methods, i.e., the masked self-attention mechanism and the gated recurrent unit. To further exploit the synergy between two sequence models, we adopt the idea of online knowledge distillation and adaptively combine two branches to form a stronger teacher model, which in turn provides predictions as extra supervision for better modeling ability. Extensive experiments on four real-world benchmark datasets show that DSMKT performs excellently in predicting future learner responses.

Relation classification plays a crucial role in detecting semantic relations between annotated entities within text data, serving as a fundamental tool for knowledge structurization. In recent years, federated learning has emerged as a promising approach for training relation classification models in decentralized settings. Existing methods have focused on developing a robust server model by decoupling model training at the server from direct access to client-side text data, while taking advantage of distributed data sources. However, a significant challenge arises from the heterogeneous nature of client texts, characterized by diverse and skewed distributions of relations, which has limited the practicality of current approaches. In response to this challenge, this study introduces the concept of personalized federated relation classification, aiming to tailor strong client models to adapt to their individual data distributions. To further address the issues stemming from heterogeneous texts, a novel framework, referred to as${\sf pFedRC}$, is proposed with several optimized designs. This framework incorporates a knowledge fusion method that leverages a relation-wise weighting mechanism, and a feature augmentation approach utilizing prototypes to adaptively enhance the representations of instances associated with long-tail relations. Although federated learning can safely ensure private data unexposed, it is important to recognize that, from an information theory standpoint, there still exists a possibility for a curious server to deduce private information by analyzing the shared knowledge uploaded by clients. To enhance the privacy guarantees of the personalized federated relation classification system, this work integrates client-level differential privacy mechanism into the federated training process. According to our theoretical analysis,${\sf pFedRC}$with client-level differential privacy can realize rigorous privacy guarantees. Experimental evaluations demonstrate the superiority of the proposed${\sf pFedRC}$framework over competing baselines in various settings, illustrating that the tailored techniques effectively mitigate the challenges posed by heterogeneous text data while preserving privacy guarantees. This research contributes to the advancement of learning privacy-preserving personalized relation classification models via taking advantages of data from multiple sources.

Information diffusion prediction is a crucial task for comprehending the dissemination process of information. Although this problem has received significant attention recently, most of the state-of-the-arts primarily focus on the modelling of information cascades, while neglecting the implicit social relations between users in the social network and failing to adequately model the interrelations between the user social network and information cascades. To tackle the aforementioned issues, in this work, we propose aDual-StateHypergraphContrastiveLearning model (DSHCL). Specifically, we first propose to construct a social hypergraph based on the social network to capture the implicit social relations. Then, for capturing the cascade level correlations among users, we generate the dual-state (i.e., static and dynamic) user representations from the user social hypergraph and information cascades. Finally, we exploit contrastive learning to model the interplay between the social network and information cascades by discriminating the dual-state representations generated from them. We conduct an empirical assessment of DSHCL across four publicly available datasets, and the findings underscore the DSHCL's superiority and the efficacy of its components.

Relation classification (RC) aims to detect the semantic relation between two annotated entities in a piece of sentence, serving as an essential task in automatic knowledge graph construction. Due to the emergence of new relations, there is a recent trend to train RC models in continual settings. To overcome the catastrophic forgetting problem in continual learning, existing research is devoted in a two-stage training paradigm, fast adaptation to novel relations, and memory replay for all historical relations. These memory-replay-based methods explore different techniques to mitigate the forgetting problem of continual RC (CRC) models during the memory replay stage. However, we find that the representation space undergoes distortion due to the incoming of fresh relations in the fast adaptation phase. To address this issue, we propose using a knowledge distillation strategy and designing a margin loss, aiming to maintain the stability of the RC model during adaptation to new relations. In addition, in the second stage, with a limited number of typical memory instances available, we introduce a self-contrastive learning objective to facilitate learning a balanced decision boundary for RC. Through training in two stages, our objective is to acquire a stable representation space to encode instances for CRC. We experimentally demonstrate the superiority of our model over competing methods in various settings, and the results suggest that our tailored designs can achieve better performance in CRC.

Temporal knowledge graph alignment (TKGA) discovers the equivalent elements among heterogeneous temporal knowledge graphs (TKGs), and thus can increase the coverage of a given TKG. However, existing TKGA datasets fail to mirror the real-life challenges, and the oversimplified scenarios may even impede the fair comparison and development of the alignment solutions. To address the aforementioned issues, in this work, we propose to benchmark challenges for temporal knowledge graph alignment by establishing a new dataset, i.e., BETA, which features multi-granular temporal information, more realistic quadruple distribution, and new challenging alignment scenarios. Furthermore, we also offer a simple yet effective solution, MGTEA, to address the aforementioned challenges, which effectively models the complex structural and multi-granular temporal features to facilitate the alignment. Extensive experiments reveal that BETA indeed better mirrors the real-life challenges, and there is still room for developing more advanced solutions to address these difficulties, despite of the superior performance achieved by MGTEA.

Multi-modal Entity Alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs (MMKGs), facilitating their integration and enhancing coverage. However, current MMEA datasets have limitations, including low entity coverage, a single image per entity, high inter-image correlation, and images sourced from the same search engine, which do not reflect real-world challenges. The fair comparison and development of alignment solutions may be hindered by these oversimplified scenarios. To address this problem, in this work, we first construct M3, an MMEA benchmark equipped with multiple images from different search engines in real-world scenarios. Additionally, we design a simple and universal multi-image processing module (AMIA), which assigns varying attention weights to images associated with entities to effectively model visual information. Experimental results validate the difficulty of M3, as well as the effectiveness of AMIA. Despite the superior performance of AMIA, there is still room for developing more advanced solutions to address these difficulties. Our dataset is publicly released.

Knowledge Graph Question Generation (KGQG) is the task of generating natural language questions based on the given knowledge graph (KG). Although extensively explored in recent years, prevailing models predominantly depend on labelled data for training deep learning models or employ large parametric frameworks, e.g., Large Language Models (LLMs), which can incur significant deployment costs and pose practical implementation challenges. To address these issues, in this work, we put forward a zero-shot, multi-agent KGQG framework. This framework integrates the capabilities of LLMs with small models to facilitate cost-effective, high-quality question generation. In specific, we develop a professional editorial team architecture accompanied by two workflow optimization tools to reduce unproductive collaboration among LLMs-based agents and enhance the robustness of the system. Extensive experiments demonstrate that our proposed framework derives the new state-of-the-art performance on the zero-shot KGQG tasks, with relative gains of 20.24% and 13.57% on two KGQG datasets, respectively, which rival fully supervised state-of-the-art models.

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches$KGs$in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.

Network dismantling is a process of identifying influential nodes that can decompose a network into disconnected sub-networks. This provides a novel approach to understanding and analyzing complex networks abstracted from the real world. State-of-the-art solutions for this task exploit graph encoders to capture the structural features of the network, which are then sent to the multi-layer perceptron for predicting the node importance. This process, however, fails to exploit the interactions among the graph representations learned from different views and neglects the neighboring information when evaluating node importance. In this work, we address these issues with a graph contrastive learning framework with multi-hop aggregation, resulting in the identification of influential nodes. Firstly, we construct role graphs to provide a holistic view of the original graphs. Secondly, graph representations are obtained in the individual views, and enhanced expressiveness is achieved through contrastive learning. Finally, based on the representations, the multi-hop neighbor information of the nodes is aggregated to rank the node importance, and thus aid in the identification of important nodes. We evaluate our proposal on real and synthetic networks, and the results show that our method outperforms the baseline with fewer nodes required to disassemble a network.

Abstract Relation extraction has evolved from supervised relation extraction to zero-shot setting due to the continuous emergence of newly generated relations. Some pioneering works handle zero-shot relation extraction by reformulating it into proxy tasks, such as reading comprehension and textual entailment. Nonetheless, the divergence in proxy task formulations from relation extraction hinders the acquisition of informative semantic representations, leading to subpar performance. Therefore, in this paper, we take a data-driven view to handle zero-shot relation extraction under a three-step paradigm, including encoder training, relation clustering, and summarization. Specifically, to train a discriminative relational encoder, we propose a novel selective contrastive learning framework, namely, SCL, where selective importance scores are assigned to distinguish the importance of different negative contrastive instances. During testing, the prompt-based encoder is employed to map test samples into representation vectors, which are then clustered into several groups. Typical samples closest to the cluster centroid are selected for summarization to generate the predicted relation for all samples in the cluster. Moreover, we design a simple non-parametric threshold plugin to reduce false-positive errors in inference on unseen relation representations. Our experiments demonstrate that SCL outperforms the current state-of-the-art method by over 3% across all metrics.

Although various graph contrastive learning (GCL) techniques have been employed to generate augmented views and maximize their mutual information, current solutions only consider the pairwise relationships based on edges, neglecting the high-order information that can help generate more informative augmented views and make better contrast. To fill in this gap, we propose to leverage hyperedge to facilitate GCL, as it connects two or more nodes and can model high-order relationships among multiple nodes. More specifically, hyperedges are constructed based on the original graph. Then, we conduct node-level PageRank based on hyperedges and hyperedge-level PageRank based on nodes to generate augmented views. As to the contrasting stage, different from existing GCL methods that simply treat the corresponding nodes of the anchor in different views as positives and overlook certain nodes strongly associated with the anchor, we build the positives and negatives based on hyperedges, where whether a node is a positive is determined by the number of hyperedges it coexists with the anchor. We compare our hyperedge GCL with state-of-the-art methods on downstream tasks, and the empirical results validate the superiority of our proposal. Further experiments on graph augmentation and graph contrastive loss also demonstrate the effectiveness of the proposed modules.

Over recent years, a number of knowledge graphs (KGs) have emerged. Nevertheless, a KG can never reach full completeness. A viable approach to increase the coverage of a KG is KG alignment (KGA). The majority of previous efforts merely focus on the matching between entities, while largely neglect relations. Besides, they heavily rely on labeled data, which are difficult to obtain in practice. To address these issues, in this work, we put forward a general framework to simultaneously align entities and relations under scarce supervision. Our proposal consists of two main components, relation-enhanced active instance selection (RAS), and cross-view contrastive learning (CCL). RAS aims to select the most valuable instances to be labeled with the guidance of relations, while CCL contrasts cross-view representations to augment scarce supervision signals. Our proposal is agnostic to the underlying entity and relation alignment models, and can be used to improve their performance under limited supervision. We conduct experiments on a wide range of popular KG pairs, and the results demonstrate that our proposed model and its components can consistently boost the alignment performance under scarce supervision.

Automatic detection of fake news has received widespread attentions over recent years. A pile of efforts has been put forward to address the problem with high accuracy, while most of them lack convincing explanations, making it difficult to curb the continued spread of false news in real-life cases. Although some models leverage external resources to provide preliminary interpretability, such external signals are not always available. To fill in this gap, in this work, we put forward an interpretable fake news detection model IKA by making use of the historical evidence in the form of graphs. Specifically, we establish both positive and negative evidence graphs by collecting the signals from the historical news, i.e., training data. Then, given a piece of news to be detected, in addition to the common features used for detecting false news, we compare the news and evidence graphs to generate both the matching vector and the related graph evidence for explaining the prediction. We conduct extensive experiments on both Chinese and English datasets. The experiment results show that the detection accuracy of IKA exceeds the state-of-the-art approaches and IKA can provide useful explanations for the prediction results. Besides, IKA is general and can be applied on other models to improve their interpretability.

Relation classification detects the semantic relation between two annotated entities from a piece of text, which is a useful tool for structurization of knowledge. Recently, federated learning has been introduced to train relation classification models in decentralized settings. Current methods strive for a strong server model by decoupling the model training at server from direct access to texts at clients while taking advantage of them. Nevertheless, they overlook the fact that clients have heterogeneous texts (i.e., texts with diversely skewed distribution of relations), which renders existing methods less practical. In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. To further meet the challenges brought by heterogeneous texts, we present a novel framework, namely pf-RC, with several optimized designs. It features a knowledge aggregation method that exploits a relation-wise weighting mechanism, and a feature augmentation method that leverages prototypes to adaptively enhance the representations of instances of long-tail relations. We experimentally validate the superiority of pf-RC against competing baselines in various settings, and the results suggest that the tailored techniques mitigate the challenges.

Node importance estimation is a fundamental task in graph analysis, which can be applied to various downstream applications such as recommendation and resource allocation. However, existing studies merely work under a single view, which neglects the rich information hidden in other aspects of the graph. Hence, in this work, we propose a Multiview Contrastive Representation Learning (MCRL) model to obtain representations of nodes from multiple perspectives and then infer the node importance. Specifically, we are the first to apply the contrastive learning technique to the node importance analysis task, which enhances the expressiveness of graph representations and lays the foundation for importance estimation. Moreover, based on the improved representations, we generate the entity importance score by attentively aggregating the scores from two different views, i.e., node view and node‐edge interaction view. We conduct extensive experiments on real‐world datasets, and the experimental results show that MCRL outperforms existing methods on all evaluation metrics.

Entity alignment aims to identify equivalent entity pairs from different knowledge graphs (KGs). Recently, aligning temporal knowledge graphs (TKGs) that contain time information has aroused increasingly more interest, as the time dimension is widely used in real-life applications. The matching between TKGs requires seed entity pairs, which are lacking in practice. Hence, it is of great significance to study TKG alignment under scarce supervision. In this work, the authors formally formulate the problem of TKG alignment with limited labeled data and propose to solve it under the active learning framework. As the core of active learning is to devise query strategies to select the most informative instances to label, the authors propose to make full use of time information and put forward novel time-aware strategies to meet the requirement of weakly supervised temporal entity alignment. Extensive experimental results on multiple real-world datasets show that it is important to study TKG alignment with scarce supervision, and the proposed time-aware strategy is effective.

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance ofentity representation learning, while largely overlook the subsequent stage thatmatches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.

Few-shot Relational Triple Extraction (RTE) aims at detecting emerging relation types along with their entity pairs from unstructured text with the support of a few labeled samples. Prior arts use conditional random field or nearest-neighbor matching strategy to extract entities and use prototypical networks for extracting relations from sentences. Nevertheless, they fail to utilize the triple-level information to verify the plausibility of extracted relational triples, and ignore the proper transfer among the perspectives of entity, relation and triple. To fill in these gaps, in this work, we put forward a novel perspective transfer network (PTN) to address few-shot RTE. Specifically, PTN starts from the relation perspective by checking the existence of a given relation. Then, it transfers to the entity perspective to locate entity spans with relation-specific support sets. Next, it transfers to the triple perspective to validate the plausibility of extracted relational triples. Finally, it transfers back to the relation perspective to check the next relation, and repeats the aforementioned procedure. By transferring among the perspectives of relation, entity, and triple, PTN not only validates the extracted elements at both local and global levels, but also effectively handles more realistic and difficult few-shot RTE scenarios such as multiple triple extraction and nonexistence of triples. Extensive experimental results on existing dataset and new datasets demonstrate that our approach can significantly improve performance over the state-of-the-arts.

Abstract Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance. Furthermore, noticing that the pseudo-labeled data are of various qualities, we introduce the concept of confidence to measure the probability of an entity pair of being true and develop a confidence-based unsupervised EA framework . Our solutions do not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate the superiority of our proposals .

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research.

Entity alignment (EA) is the task of detecting equivalent entities from different knowledge graphs (KGs). Although this problem has been intensively studied during the last few years, the majority of the state-of-the-arts heavily rely on the labeled data, which are difficult to obtain in practice. Therefore, it calls for the study of EA with scarce supervision. To resolve this issue, we put forward a reinforced active entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised contrastive loss to contrast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.

Knowledge graphs are typical large-scale multi-relational structures and useful for many artificial intelligence tasks. However, knowledge graphs often have missing facts, which limits the development of downstream tasks. To refine the knowledge graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models aim to learn distributed representations for entities and relations and predict unknown triplets by scoring candidate triplets. Nevertheless, state-of-the-art works either aim to capture different relation patterns, or to model the multi-fold relations, and yet fail to consider these two aspects simultaneously. To fill this gap, in this paper, we propose a novel knowledge graph embedding model, MRotatE. It exploits triplet features from the perspective of relational and entity rotations, which can model and infer various relation patterns and handle with multi-fold relations at the same time. The experimental results demonstrate that MRotatE outperforms existing approaches and attains the state-of-the-art performance.

Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be aligned, existing EA solutions treat them separately and generate alignment results as ranked lists of entities on the other side. Nevertheless, this decision-making paradigm fails to take into account the interdependence among entities. Although some recent efforts mitigate this issue by imposing the 1-to-1 constraint on the alignment process, they still cannot adequately model the underlying interdependence and the results tend to be sub-optimal. To fill in this gap, in this work, we delve into the dynamics of the decision-making process, and offer a reinforcement learning (RL)–based model to align entities collectively. Under the RL framework, we devise the coherence and exclusiveness constraints to characterize the interdependence and restrict collective alignment. Additionally, to generate more precise inputs to the RL framework, we employ representative features to capture different aspects of the similarity between entities in heterogeneous KGs, which are integrated by an adaptive feature fusion strategy. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks and compared against state-of-the-art solutions. The empirical results verify its effectiveness and superiority.

Abstract With the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose , a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs—the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model , comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of over state-of-the-art methods.

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.

Entity linking, as one of the fundamental tasks in natural language processing, is crucial to knowledge fusion, knowledge base construction and update. Nevertheless, in contrast to the research on entity linking for English text, which undergoes continuous development, the Chinese counterpart is still in its infancy. One prominent issue lies in publicly available annotated datasets and evaluation benchmarks, which are lacking and deficient. In specific, existing Chinese corpora for entity linking were mainly constructed from noisy short texts, such as microblogs and news headings, where long texts were largely overlooked, which yet constitute a wider spectrum of real-life scenarios. To address the issue, in this work, we build CLEEK, a Chinese corpus of multi-domain long text for entity linking, in order to encourage advancement of entity linking in languages besides English. The corpus consists of 100 documents from diverse domains, and is publicly accessible. Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus. Additionally, the results of two baselines and seven state-of-the-art solutions on CLEEK are reported and compared. The empirical results validate the usefulness of CLEEK and the effectiveness of proposed difficulty measure.

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA.